Gemral Edge

Machine Probability Forecast

get_forecast

Quantitative probabilistic modeling forecasting sector-relative return distributions, confidence intervals, and realized outcome benchmarks with explicit is_backtest provenance flag.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "ticker": "<ticker>"
    },
    "name": "get_forecast"
  }
}

Input Schema

Name	Required	Description	Default
`ticker`	Yes	Stock ticker, e.g. PLTR, NVDA
`outcome_definition_id`	No	Canonical outcome definition ID (optional)

Output Schema

Name	Required	Description
`notice`	No	Free-tier upgrade notice; only when results are masked/delayed.
`results`	Yes	Machine probability forecast payload with explicit is_backtest provenance flag.
`history_limit`	No	Present when the license limits history depth: earliest_allowed date, history_days and how many older items were removed.
`deprecated_fields`	No	Present only while a renamed key is still returned next to its replacement: field (old key), replacement (new key, same value), removal (ISO date after which the old key is gone).

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedOct 26, 2026

addedOutput schema / properties / deprecated_fields

Added value: +{
+  "description": "Present only while a renamed key is still returned next to its replacement: field (old key), replacement (new key, same value), removal (ISO date after which the old key is gone).",
+  "items": {
+    "additionalProperties": true,
+    "type": "object"
+  },
+  "type": "array"
+}

First observedOct 23, 2026

TDQS

C2.8/5.0

Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations declare readOnlyHint=false on a get_-prefixed tool, which is surprising and unexplained — the description never says whether it computes on demand, persists anything, or incurs cost/latency. The only real behavioral disclosure is the is_backtest provenance flag, which usefully tells the agent backtested vs live results may be mixed, but the non-read-only nature remains a gap.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

A single sentence with no filler, but it is a dense noun pile ('Quantitative probabilistic modeling forecasting sector-relative return distributions') that front-loads jargon rather than the actionable trigger for calling the tool.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

An output schema exists, so return values needn't be described. However, for a non-read-only tool with two parameters and dozens of sibling analytics tools, the absence of any when-to-use context, cost/behavior notes, or parameter explanation leaves the definition materially incomplete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so both ticker and outcome_definition_id are documented in the schema and the baseline is 3. The description mentions no parameters and never clarifies what an outcome_definition_id selects, so it adds nothing beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description names a specific capability: probabilistic forecasting of sector-relative return distributions with confidence intervals and realized-outcome benchmarks. An agent can tell this produces a forecast rather than raw historical data, though it never distinguishes itself from adjacent siblings like get_signal_historical_outcomes or get_track_record.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no guidance on when to call this versus the many look-alike siblings. No prerequisites, no conditions, no exclusions — the agent must infer that this is the 'forward-looking quantitative' option purely from the phrase 'forecasting'.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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